ABSTRACT
Natural language comprehension involves processing a multitude of words that vary along many dimensions, some of which may reflect statistical regularities in language. These variables may differ in their relative importance across various types of words and tasks. This study used a multiple regression approach to investigate potentially important predictors of noun-verb processing across naming, grammatical categorization, and sentence completion tasks. Although there were some indications of different predictors for nouns vs verb processing, the strongest predictors of response latencies were primarily determined by the types of processing most important for a given task. One variable of particular interest was the newly created Noun-Verb Distributional Difference (NVDD) metric developed by Chiarello et al. (1999). NVDD values reflect statistical regularities in language regarding the typicality of the contexts in which nouns and verbs tend to occur. The results suggest that although noun-verb typicality, as assessed via the NVDD, is a valid measure of regularities in noun-verb contexts within a linguistic corpus, individuals may not be very sensitive to this dimension in standard psycholinguistic processing tasks.
